Your agent is only as safe as the data you test it against.
SynthData adaptively generates the synthetic inputs and edge cases most likely to make your AI agent behave unsafely or off-policy — so you find the failures before your customers do.
Static tests give false confidence.
Real production traffic is unpredictable. Hand-written test suites miss the edge cases that actually cause failures, while real sensitive customer data is unsafe to use for testing.
Production behavior is unpredictable
The dangerous edge cases are rarely hand-written
Sensitive customer data cannot be your test set
A closed loop that learns where your agent is weak.
Adaptive generation is the differentiator. SynthData does not stop at a fixed suite — it observes the agent, then increases pressure where risk appears.
Understand
Learns your agent's purpose, policies, and operational boundaries.
Generate
Creates synthetic data spanning normal, edge, and adversarial cases without real customer data.
Adapt
Watches for weaknesses and generates more pressure where the agent shows risk, converging on failure modes.
Report
Surfaces concrete policy violations and unsafe behaviors with reproducible test cases.
Rigor without exposing production data.
Find the edge cases static suites miss
Adaptive generation targets the conditions most likely to make your agent fail.
Test without sensitive customer data
Generate realistic, rigorous inputs without exposing production records.
Prove safety before go-live
Turn failures into reproducible evidence for security, engineering, and GRC teams.
See where your agent breaks before go-live.
Bring a policy, an agent, or a difficult test case. We will show you how SynthData adapts to the risk.